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    政大機構典藏 > 資訊學院 > 資訊科學系 > 會議論文 >  Item 140.119/69112


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    題名: A Recommendation Mechanism on OLAP Operations Based on Social Network
    其他題名: 整合社群關係的 OLAP 操作推薦機制
    作者: 陳信固;李蔡彥
    Chen,H.K.;Li,Tsai-yen
    貢獻者: 資科系
    關鍵詞: 社群網路分析;推薦機制;社群偵測;商業智慧;網絡中心性
    Social Network Analysis;Recommendation Mechanism;Community Detection;Business Intelligence;Network Centrality
    日期: 2012-05
    上傳時間: 2014-08-21 11:04:02 (UTC+8)
    摘要: 近幾年在金融風暴及全球競爭等影響下，企業紛紛導入商業智慧平台，提供管理階層可簡易且快速的分析各種可量化管理的關鍵指標。但在後續的推廣上，經常會因商業智慧系統提供的資訊過於豐富，造成使用者在學習階段無法有效的取得所需資訊，導致商業智慧無法發揮預期效果。本論文以使用者在商業智慧平台上的操作相似度進行分析，建立相對於實體部門的凝聚子群，且用中心性計算各節點的關聯加權，整合至所設計的推薦機制，用以提升商業智慧平台成功導入的機率。經模擬實驗的證實，在推薦機制中考慮此因素會較原始的推薦機制擁有更高的精確度。
    In recent years, enterprises are facing financial turmoil, global competition, and shortened business cycle. Under these influences, enterprises usually implement the Business Intelligence platform to help managers get the key indicators of business management quickly and easily. In the promotion stage of such Business Intelligence platforms, users usually give up using the system due to huge amount of information provided by the BI platform. They cannot intuitively obtain the required information in the early stage when they use the system. In this study, we analyze the similarity of users’ operations on the BI platform and try to establish cohesive subgroups in the corresponding organization. In addition, we also integrate the associated weighting factor calculated from the centrality measures into the recommendation mechanism to increase the probability of successful uses of BI platform. From our simulation experiments, we find that the recommendation accuracies are higher when we add the clustering result and the associated weighting factor into the recommendation mechanism.
    關聯: Appeared in Proceeding of the 2012 International Conference on Information Management, 2012.
    資料類型: conference
    顯示於類別:[資訊科學系] 會議論文

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